Open AccessDynamic weighting in Monte Carlo and optimization
Author(s) -
Wing Hung Wong,
Faming Liang
Publication year - 1997
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.94.26.14220
Subject(s) - weighting , monte carlo method , maxima and minima , travelling salesman problem , mathematical optimization , computer science , artificial neural network , energy (signal processing) , sampling (signal processing) , hybrid monte carlo , sample (material) , algorithm , mathematics , artificial intelligence , markov chain monte carlo , statistics , physics , acoustics , filter (signal processing) , mathematical analysis , thermodynamics , computer vision
Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom